The Interplay of Fiscal Policy, Economic Behavior, and Technological Innovation

This thesis contains three chapters relevant to fiscal policy. The first chapter focuses on the effect of tax news shocks on economic aggregates, firm behavior, and financial markets. The second chapter addresses how firms’ total factor productivity with different levels of market power responds to fiscal consolidation shocks. The last chapter emphasizes the identification of occupations that are exposed to and potentially displaced by or benefited from artificial intelligence, which helps fiscal policy cushion the negative labor market and distributional effects to broaden the gains.

The first essay focuses on the economic consequences of “anticipated” shocks to taxes, specifically news or foresight about future shifts in tax policy. I apply an advanced topic model known as seeded Latent Dirichlet Allocation (LDA). This model offers advantages over unsupervised LDA and lexicon-based methods, providing a more nuanced interpretation of tax policy discourse. By analyzing a large corpus of news articles and presidential documents, I create two indices that separately quantify the intensity of news related to tax increases and decreases over time, starting from the Truman administration. Comparative analysis indicates that the metrics derived from seeded LDA are reliable. They can effectively forecast tax shocks as identified in the narrative-based approach by Romer and Romer (2010), as well as a commonly cited tax news measure by Leeper et al. (2012). The study incorporates both macroeconomic and firm-level perspectives, presenting empirical evidence that tax foresight significantly influences economic aggregates, corporate behavior, and financial markets. First, news of tax hikes stimulates output in the short term, and the effect of such news varies across different economic states. Second, firms with greater market power are more reactive to news of tax hikes, accelerating investment at a faster pace than their counterparts. Third, firms that are heavily reliant on government purchases show increased stock price volatility when attention to tax policy among investors is high. Finally, both stock and bond markets exhibit immediate responses when there is news of tax hikes, as these are viewed as steps toward fiscal responsibility and sustainable economic growth.

The second essay examines the heterogeneous effects of fiscal consolidation shocks on firm-level total factor productivity, with a particular focus on the role of market power. Using a comprehensive dataset of 1.6 million firms across 11 European countries from 2000 to 2019, I employ local projection methods to estimate the dynamic effects of fiscal consolidation on firm productivity. The main finding reveals that firms with high market power experience significant and persistent declines in TFP following fiscal consolidation shocks, with reductions of up to 4 percent, while low-markup firms show little change or slight improvements. This differential response is robust to controlling for other firm characteristics such as size, age, leverage, and asset tangibility. The negative impact on high-markup firms is more pronounced during economic downturns and in response to tax-based consolidations rather than spending cuts. Further analysis suggests that high-markup firms tend to absorb cost increases resulting from fiscal consolidation rather than passing them on to consumers, potentially explaining their productivity decline. These findings have important implications for the design and timing of fiscal consolidation measures, highlighting the need to consider the distribution of market power within an economy when implementing fiscal policy. The results contribute to the literature on the transmission of fiscal policy and its interaction with market structure, offering valuable insights for both academic research and policy formulation in the post-pandemic economic landscape.

The third essay is a joint project with Carlo Pizzinelli, Augustus Panton, Marina M. Tavares, and Mauro Cazzaniga. It examines the impact of Artificial Intelligence (AI) on labor markets in both Advanced Economies (AEs) and Emerging Markets (EMs). We propose an extension to a standard measure of AI exposure, accounting for AI’s potential as either a complement or a substitute for labor, where complementarity reflects lower risks of job displacement. We analyze worker-level microdata from 2 AEs and 4 EMs, revealing substantial cross-country variation in unadjusted AI exposure. AEs face higher exposure than EMs due to a higher employment share in professional and managerial occupations. However, when accounting for potential complementarity, differences in exposure are more muted. Within countries, common patterns emerge in AEs and EMs. Women and highly educated workers face greater occupational exposure to AI, at both high and low complementarity. Workers in the upper tail of the earnings distribution are more likely to be in occupations with high exposure but also high potential complementarity. This forward-looking analysis helps to inform the design of targeted fiscal interventions that can address emerging inequalities, support worker transitions, and harness the productivity gains offered by AI technologies. Given the multiple authorship of this essay, my specific contributions are as follows: 1) I contributed to conceptualizing the link between AI exposure and AI complementarity at the occupational level, which is a key distinguishing feature of our study; 2) I led the team in selecting, collecting, and processing the data, and in designing the empirical strategy for synthesizing inputs to develop the complementarity-adjusted AI occupational exposure index; 3) I was responsible for the design and implementation of a comprehensive set of robustness checks for the measure; and 4) I drafted the relevant sections of the essay. In summary, I played a key role in developing the first of the two major contributions made by this essay.

By integrating these three perspectives, this thesis provides a nuanced understanding of how fiscal policy interacts with economic behavior and technological progress. The findings offer valuable insights for policymakers navigating the complexities of tax policy design, fiscal consolidation strategies, and the challenges posed by rapid technological change in the evolving economic landscape.